Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc...
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Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc...

AI Smartphones Will Soon Be Standard, All Thanks to Machine Learning Chip

Almost every major player in the smartphone industry says that their devices use the power of artificial intelligence (AI), or machine learning algorithms. Few devices, however, run their own AI software. Now, AI smartphones could one day be standard due to processor developed and dedicated to machine learning for mobile phones and other smart-home devices.

ARM, the manufacturing firm of every chip, now wants to put the power of AI into every mobile device. Currently, devices that run AI algorithms depend on servers in the cloud. It's a rather limited set up, with online connectivity affecting how information is sent back and forth.

Project Trillium would make this process much more efficient. Their built-in AI chip would allow devices to continue running machine learning algorithms even when offline. This reduces data traffic and speeds up processing, while also saving power.

"We analyze compute workloads, work out which bits are taking the time and the power, and look to see if we can improve on our existing processors," says Jem Davies, ARM's group head in Machine Learning. Running machine learning algorithms means fewer chances of data slipping through.

Machine learning shows the future of mobile computing. Apple has already designed and built a "neural engine" as part of the iPhone X's main chipset, to handle the phone's artificial neural networks for images and speech processing.

Google's own chipset, for their Pixel 2 smartphone, does something similar. Huawei's Mate 10 model packs a neural processing unit developed by the Chinese smartphone maker. Amazon might follow soon with its own AI chips for Alexa as well.

ARM's track record for energy-efficient mobile processors could translate to a more widespread adoption of their AI chip. It doesn't actually make the chips they design, so the company has started sharing their plans for this AI chip to their hardware partners like smartphone chipmaker Qualcomm. They are expecting to find their machine learning processor in devices by early 2019.